{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# The Image Classification Dataset\n",
    "\n",
    "The MNIST dataset is one of the widely used dataset for image classification, while it's too simple as a benchmark dataset. We will use the similar, but more complex Fashion-MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "origin_pos": 3,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "from d2l import torch as d2l\n",
    "\n",
    "d2l.use_svg_display()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Download and load the Fashion-MNIST dataset into memory using built-in framework functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class FashionMNIST(d2l.DataModule):  \n",
    "    def __init__(self, batch_size=64, resize=(28,28)):\n",
    "        super().__init__()\n",
    "        self.save_hyperparameters()\n",
    "        trans = transforms.Compose([transforms.Resize(resize),\n",
    "                                    transforms.ToTensor()])\n",
    "        self.train = torchvision.datasets.FashionMNIST(\n",
    "            root=self.root, train=True, transform=trans, download=True)\n",
    "        self.val = torchvision.datasets.FashionMNIST(\n",
    "            root=self.root, train=False, transform=trans, download=True)\n",
    "\n",
    "data = FashionMNIST(resize=(32,32))\n",
    "len(data.train), len(data.val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "origin_pos": 13,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 32, 32])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.train[0][0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Two utility functions to visualize the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "origin_pos": 15,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "@d2l.add_to_class(FashionMNIST)  \n",
    "def text_labels(self, indices):\n",
    "    \"\"\"Return text labels.\"\"\"\n",
    "    labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "              'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return [labels[int(i)] for i in indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Reads a minibatch of data with size `batch_size`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "origin_pos": 21,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 1, 32, 32]) torch.float32 torch.Size([64]) torch.int64\n"
     ]
    }
   ],
   "source": [
    "@d2l.add_to_class(FashionMNIST)  \n",
    "def get_dataloader(self, train):\n",
    "    data = self.train if train else self.val\n",
    "    return torch.utils.data.DataLoader(data, self.batch_size, shuffle=train,\n",
    "                                       num_workers=self.num_workers)\n",
    "\n",
    "X, y = next(iter(data.train_dataloader()))\n",
    "print(X.shape, X.dtype, y.shape, y.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "origin_pos": 23,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.95 sec'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tic = time.time()\n",
    "for X, y in data.train_dataloader():\n",
    "    continue\n",
    "f'{time.time() - tic:.2f} sec'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "origin_pos": 25,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):  \n",
    "    \"\"\"Plot a list of images.\"\"\"\n",
    "    raise NotImplementedError"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The images and their corresponding labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "origin_pos": 27,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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      "text/plain": [
       "<Figure size 864x108 with 8 Axes>"
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     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "@d2l.add_to_class(FashionMNIST)  \n",
    "def visualize(self, batch, nrows=1, ncols=8, labels=[]):\n",
    "    X, y = batch\n",
    "    if not labels:\n",
    "        labels = self.text_labels(y)\n",
    "    d2l.show_images(X.squeeze(1), nrows, ncols, titles=labels)\n",
    "batch = next(iter(data.val_dataloader()))\n",
    "data.visualize(batch)"
   ]
  }
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